Senior Data Engineer - Data Science Platform

Designing, building, and maintaining scalable data pipelines using Python and Scala , leveraging Spark and PySpark within an Azure ecosystem (including Azure Data Factory and Databricks ).

Developing and maintaining reusable data engineering templates, frameworks, and tooling to support data teams across ASOS.

Driving standardisation and best practices across data ingestion, transformation, and serving layers to ensure consistency across diverse product domains.

Enabling teams to deliver high-quality, production-ready datasets by providing guidance, patterns, and hands-on technical support.

Implementing and promoting modern data engineering practices — including CI/CD for data pipelines, data quality validation, testing, observability, and metadata management.

Collaborating with stakeholders to understand data requirements and evolving the data platform to meet business needs.

Partnering with Platform Engineering, ML Engineering, and Security teams to ensure scalable, cost-efficient, and secure data infrastructure on Azure.

Optimising data workflows and pipelines for performance, reliability, and cost efficiency.

We believe being together in person helps us move faster, connect more deeply, and achieve more as a team. That’s why our approach to working together includes spending at least 2 days a week in the office. It’s a rhythm that speeds up decision-making, helps ASOSers learn from each other more quickly, and builds the kind of culture where people can grow, create, and succeed.

Strong experience as a Data Engineer building scalable data platforms

Deep expertise in Azure (ADF, ADLS, Databricks)

Proficiency in Python and/or Scala (PySpark/Spark) for large-scale data processing

Hands-on experience with Databricks and Delta Lake

Solid understanding of the end-to-end data lifecycle (ingestion → transformation → serving)

Experience with dbt for transformations and Terraform for infrastructure as code